![]() ![]() Vascular diseases can primarily be divided into several categories based on the type of vessel. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.Īngiology, which concerns vessel-related diseases, is one of the most important branches of medical science since vascular diseases are very common and cause death to a large number of people every year . In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. ![]() In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. Second, SVD is prone to failure in the presence of large random noise in the dataset. ![]() First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. This approach exhibits two well-known limitations. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. Therefore, a clear visualization of vasculature is of high clinical significance. Vessel diseases are often accompanied by abnormalities related to vascular shape and size. ![]()
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